A high performance, low-latency backtesting engine for testing quantitative trading strategies on historical and live data in Rust. 235 – Computational tools for financial economics include: Gaussian Mixture model of leptokurtotic risk, adaptive Boltzmann portfolios. Common financial risk. Performance metrics. Financial Risk Calculations. Optimized for ease of use through class construction and operator overload. Portfolio and risk analytics in Python. Quantitative Financial Risk Management: awesome OOP tools for measuring, managing and visualizing risk of financial instruments and portfolios.

Trading Interest Rate Derivatives. Tidy Finance – An opinionated approach to empirical research in financial economics – a fully transparent, open-source code base in multiple programming languages (Python and R) to enable the reproducible implementation of financial research projects for students and practitioners. AFML – All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo. Implementation of 101 formulaic alphas using qstrader. RoughVolatilityWorkshop – 2024 QuantMind’s Rough Volatility Workshop lectures. Python programs to help you gather, manipulate, and analyze stock market data.

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StockSharp – Algorithmic trading and quantitative trading open source platform to develop trading robots – https://www.pipihosa.com/2023/11/21/after-cz-quits-as-binance-ceo-richard-teng-looks-like-the-heir-apparent/ – (stock markets, forex, crypto, bitcoins, and options). TDAmeritrade.DotNetCore – Free, open-source .NET Client for the TD Ameritrade Trading Platform. TradeAggregation – Aggregate trades into user-defined candles using information driven rules. RustQuant – Quantitative finance library written in Rust. SlidingFeatures – Chainable tree-like sliding windows for signal processing and technical analysis. Helps developers integrate TD Ameritrade API into custom trading solutions. LFEST – Simulated perpetual futures exchange to trade your strategy against.

A rust library for financial data analysis. Derman Papers – Notebooks that replicate original quantitative finance papers from Emanuel Derman. Pricing – An library to price financial options written in Python. ML-Quant – Top Quant resources like ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs. A complete set of volatility estimators based on Euan Sinclair’s Volatility Trading. 235 – Open source project for software tools in financial economics. Many jupyter notebook to verify theoretical ideas. Quantitative Finance and Algorithmic Trading exhaust; mostly ipython notebooks based on Quantopian, Zipline, or Pandas. Auto-Differentiation Website – Background and resources on Automatic Differentiation (AD) / Adjoint Algorithmic Differentitation (AAD).

A JavaScript library for common financial calculations.

MarketTechnicals.jl – Technical analysis of financial time series on top of TimeSeries. MarketData.jl – Time series market data. Java library with algorithms. DRIP – Fixed Income, Asset Allocation, Transaction Cost Analysis, XVA Metrics Libraries. A JavaScript library for common financial calculations. Methodologies related to mathematical finance. JQuantLib – JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java. Free Java components for Quantitative Finance and Algorithmic Trading. TimeFrames.jl – A Julia library that defines TimeFrame (essentially for resampling TimeSeries). 4j – A Java library for technical analysis. Strata – Modern open-source analytics and market risk library designed and written in Java. Ghostfolio – Wealth management software to keep track of financial assets like stocks, ETFs or cryptocurrencies and make solid, data-driven investment decisions.

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